Biblio

Recently, conversation agents have attracted the attention of many companies such as IBM, Facebook, Google, and Amazon which have focused on developing tools or API (Application Programming Interfaces) for developers to create their own chat-bots. In this paper, we focus on new approaches to evaluate such systems presenting some recommendations resulted from evaluating a real chatbot use case. Testing conversational agents or chatbots is not a trivial task due to the multitude aspects/tasks (e.g., natural language understanding, dialog management and, response generation) which must be considered separately and as a mixture. Also, the creation of a general testing tool is a challenge since evaluation is very sensitive to the application context. Finally, exhaustive testing can be a tedious task for the project team what creates a need for a tool to perform it automatically. This paper opens a discussion about how conversational systems testing tools are essential to ensure well-functioning of such systems as well as to help interface designers guiding them to develop consistent conversational interfaces.

An operating system kernel written in the Rust language would have extremely fine-grained isolation boundaries, have no memory leaks, and be safe from a wide range of security threats and memory bugs. Previous efforts towards this end concluded that writing a kernel requires changing Rust. This paper reaches a different conclusion, that no changes to Rust are needed and a kernel can be implemented with a very small amount of unsafe code. It describes how three sample kernel mechanisms–-DMA, USB, and buffer caches–-can be built using these abstractions.

Trust in SSL-based communications is provided by Certificate Authorities (CAs) in the form of signed certificates. Checking the validity of a certificate involves three steps: (i) checking its expiration date, (ii) verifying its signature, and (iii) ensuring that it is not revoked. Currently, such certificate revocation checks are done either via Certificate Revocation Lists (CRLs) or Online Certificate Status Protocol (OCSP) servers. Unfortunately, despite the existence of these revocation checks, sophisticated cyber-attackers, may trick web browsers to trust a revoked certificate, believing that it is still valid. Consequently, the web browser will communicate (over TLS) with web servers controlled by cyber-attackers. Although frequently updated, nonced, and timestamped certificates may reduce the frequency and impact of such cyber-attacks, they impose a very large overhead to the CAs and OCSP servers, which now need to timestamp and sign on a regular basis all the responses, for every certificate they have issued, resulting in a very high overhead. To mitigate this overhead and provide a solution to the described cyber-attacks, we present CCSP: a new approach to provide timely information regarding the status of certificates, which capitalizes on a newly introduced notion called signed collections. In this paper, we present the design, preliminary implementation, and evaluation of CCSP in general, and signed collections in particular. Our preliminary results suggest that CCSP (i) reduces space requirements by more than an order of magnitude, (ii) lowers the number of signatures required by 6 orders of magnitude compared to OCSP-based methods, and (iii) adds only a few milliseconds of overhead in the overall user latency.

Nearly all modern software has security flaws–-either known or unknown by the users. However, metrics for evaluating software security (or lack thereof) are noisy at best. Common evaluation methods include counting the past vulnerabilities of the program, or comparing the size of the Trusted Computing Base (TCB), measured in lines of code (LoC) or binary size. Other than deleting large swaths of code from project, it is difficult to assess whether a code change decreased the likelihood of a future security vulnerability. Developers need a practical, constructive way of evaluating security. This position paper argues that we actually have all the tools needed to design a better, empirical method of security evaluation. We discuss related work that estimates the severity and vulnerability of certain attack vectors based on code properties that can be determined via static analysis. This paper proposes a grand, unified model that can predict the risk and severity of vulnerabilities in a program. Our prediction model uses machine learning to correlate these code features of open-source applications with the history of vulnerabilities reported in the CVE (Common Vulnerabilities and Exposures) database. Based on this model, one can incorporate an analysis into the standard development cycle that predicts whether the code is becoming more or less prone to vulnerabilities.

In video surveillance, face recognition (FR) systems seek to detect individuals of interest appearing over a distributed network of cameras. Still-to-video FR systems match faces captured in videos under challenging conditions against facial models, often designed using one reference still per individual. Although CNNs can achieve among the highest levels of accuracy in many real-world FR applications, state-of-the-art CNNs that are suitable for still-to-video FR, like trunk-branch ensemble (TBE) CNNs, represent complex solutions for real-time applications. In this paper, an efficient CNN architecture is proposed for accurate still-to-video FR from a single reference still. The CCM-CNN is based on new cross-correlation matching (CCM) and triplet-loss optimization methods that provide discriminant face representations. The matching pipeline exploits a matrix Hadamard product followed by a fully connected layer inspired by adaptive weighted cross-correlation. A triplet-based training approach is proposed to optimize the CCM-CNN parameters such that the inter-class variations are increased, while enhancing robustness to intra-class variations. To further improve robustness, the network is fine-tuned using synthetically-generated faces based on still and videos of non-target individuals. Experiments on videos from the COX Face and Chokepoint datasets indicate that the CCM-CNN can achieve a high level of accuracy that is comparable to TBE-CNN and HaarNet, but with a significantly lower time and memory complexity. It may therefore represent the better trade-off between accuracy and complexity for real-time video surveillance applications.

Mobility and multihoming have become the norm in Internet access, e.g. smartphones with Wi-Fi and LTE, and connected vehicles with LTE and DSRC links that change rapidly. Mobility creates challenges for active session continuity when provider-aggregatable locators are used, while multihoming brings opportunities for improving resiliency and allocative efficiency. This paper proposes a novel migration protocol, in the context of the eXpressive Internet Architecture (XIA), the XIA Migration Protocol. We compare it with Mobile IPv6, with respect to handoff latency and overhead, flow migration support, and defense against spoofing and replay of protocol messages. Handoff latencies of the XIA Migration Protocol and Mobile IPv6 Enhanced Route Optimization are comparable and neither protocol opens up avenues for spoofing or replay attacks. However, XIA requires no mobility anchor point to support client mobility while Mobile IPv6 always depends on a home agent. We show that XIA has significant advantage over IPv6 for multihomed hosts and networks in terms of resiliency, scalability, load balancing and allocative efficiency. IPv6 multihoming solutions either forgo scalability (BGP-based) or sacrifice resiliency (NAT-based), while XIA's fallback-based multihoming provides fault tolerance without a heavy-weight protocol. XIA also allows fine-grained incoming load-balancing and QoS-matching by supporting flow migration. Flow migration is not possible using Mobile IPv6 when a single IPv6 address is associated with multiple flows. From a protocol design and architectural perspective, the key enablers of these benefits are flow-level migration, XIA's DAG-based locators and self-certifying identifiers.

This talk describes how the Cyber-Physical Systems Virtual Organization (CPS-VO) is hosting competitions for the purpose of improving CPS verication tools. We describe the 2016 Challenge, which focused on quadrotor control and codesign of payload, and the 2017 Challenge which focuses on populating a ground vehicle simulator with realistic obstacles. In addition, the interfaces by which participants compete are described, in order to articulate the means by which models can be decoupled from the system for the purposes of evaluation by external tools. 

This talk describes how the Cyber-Physical Systems Virtual Organization (CPS-VO) is hosting competitions for the purpose of improving CPS verication tools. We describe the 2016 Challenge, which focused on quadrotor control and codesign of payload, and the 2017 Challenge which focuses on populating a ground vehicle simulator with realistic obstacles. In addition, the interfaces by which participants compete are described, in order to articulate the means by which models can be decoupled from the system for the purposes of evaluation by external tools. 

Based on the feature analysis of image content, this paper proposes a novel steganalytic method for grayscale images in spatial domain. In this work, we firstly investigates directional lifting wavelet transform (DLWT) as a sparse representation in compressive sensing (CS) domain. Then a block CS (BCS) measurement matrix is designed by using the generalized Gaussian distribution (GGD) model, in which the measurement matrix can be used to sense the DLWT coefficients of images to reflect the feature residual introduced by steganography. Extensive experiments are showed that proposed scheme CS-based is feasible and universal for detecting stegography in spatial domain.

Verifying the integrity of outsourced data is a classic, well-studied problem. However current techniques have fundamental performance and concurrency limitations for update-heavy workloads. In this paper, we investigate the potential advantages of deferred and batched verification rather than the per-operation verification used in prior work. We present Concerto, a comprehensive key-value store designed around this idea. Using Concerto, we argue that deferred verification preserves the utility of online verification and improves concurrency resulting in orders-of-magnitude performance improvement. On standard benchmarks, the performance of Concerto is within a factor of two when compared to state-of-the-art key-value stores without integrity.

Highly privileged software, such as firmware, is an attractive target for attackers. Thus, BIOS vendors use cryptographic signatures to ensure firmware integrity at boot time. Nevertheless, such protection does not prevent an attacker from exploiting vulnerabilities at runtime. To detect such attacks, we propose an event-based behavior monitoring approach that relies on an isolated co-processor. We instrument the code executed on the main CPU to send information about its behavior to the monitor. This information helps to resolve the semantic gap issue. Our approach does not depend on a specific model of the behavior nor on a specific target. We apply this approach to detect attacks targeting the System Management Mode (SMM), a highly privileged x86 execution mode executing firmware code at runtime. We model the behavior of SMM using invariants of its control-flow and relevant CPU registers (CR3 and SMBASE). We instrument two open-source firmware implementations: EDKII and coreboot. We evaluate the ability of our approach to detect state-of-the-art attacks and its runtime execution overhead by simulating an x86 system coupled with an ARM Cortex A5 co-processor. The results show that our solution detects intrusions from the state of the art, without any false positives, while remaining acceptable in terms of performance overhead in the context of the SMM (i.e., less than the 150 us threshold defined by Intel).

Extracting patterns and deriving insights from spatio-temporal data finds many target applications in various domains, such as in urban planning and computational sustainability. Due to their inherent capability of simultaneously modeling the spatial and temporal aspects of multiple instances, tensors have been successfully used to analyze such spatio-temporal data. However, standard tensor factorization approaches often result in components that are highly overlapping, which hinders the practitioner's ability to interpret them without advanced domain knowledge. In this work, we tackle this challenge by proposing a tensor factorization framework, called CP-ORTHO, to discover distinct and easily-interpretable patterns from multi-modal, spatio-temporal data. We evaluate our approach on real data reflecting taxi drop-off activity. CP-ORTHO provides more distinct and interpretable patterns than prior art, as measured via relevant quantitative metrics, without compromising the solution's accuracy. We observe that CP-ORTHO is fast, in that it achieves this result in 5x less time than the most accurate competing approach.

Fault attack becomes a serious threat to system security and requires to be evaluated in the design stage. Existing methods usually ignore the intrinsic uncertainty in attack process and suffer from low scalability. In this paper, we develop a general framework to evaluate system vulnerability against fault attack. A holistic model for fault injection is incorporated to capture the probabilistic nature of attack process. Based on the probabilistic model, a security metric named as System Security Factor (SSF) is defined to measure the system vulnerability. In the framework, a Monte Carlo method is leveraged to enable a feasible evaluation of SSF for different systems, security policies, and attack techniques. We enhance the framework with a novel system pre-characterization procedure, based on which an importance sampling strategy is proposed. Experimental results on a commercial processor demonstrate that compared to random sampling, a 2500X speedup is achieved with the proposed sampling strategy. Meanwhile, 3% registers are identified to contribute to more than 95% SSF. By hardening these registers, a 6.5X security improvement can be achieved with less than 2% area overhead.

Several defect prediction models proposed are effective when historical datasets are available. Defect prediction becomes difficult when no historical data exist. Cross-project defect prediction (CPDP), which uses projects from other sources/companies to predict the defects in the target projects proposed in recent studies has shown promising results. However, the performance of most CPDP approaches are still beyond satisfactory mainly due to distribution mismatch between the source and target projects. In this study, a credibility theory based Naïve Bayes (CNB) classifier is proposed to establish a novel reweighting mechanism between the source projects and target projects so that the source data could simultaneously adapt to the target data distribution and retain its own pattern. Our experimental results show that the feasibility of the novel algorithm design and demonstrate the significant improvement in terms of the performance metrics considered achieved by CNB over other CPDP approaches.

Cloud storage can provide outsourcing data services for both organizations and individuals. However, cloud storage still faces many challenges, e.g., public integrity auditing, the support of dynamic data, and low computational audit cost. To solve the problems, a number of techniques have been proposed. Recently, Tian et al. proposed a novel public auditing scheme for secure cloud storage based on a new data structure DHT. The authors claimed that their scheme was proven to be secure. Unfortunately, through our security analysis, we find that the scheme suffers from one attack and one security shortage. The attack is that an adversary can forge the data to destroy the correctness of files without being detected. The shortage of the scheme is that the updating operations for data blocks is vulnerable and easy to be modified. Finally, we give our countermeasures to remedy the security problems.

In several critical military missions, more than one decision level are involved. These decision levels are often independent and distributed, and sensitive pieces of information making up the military mission must be kept hidden from one level to another even if all of the decision levels cooperate to accomplish the same task. Usually, a mission is negotiated through insecure networks such as the Internet using cryptographic protocols. In such protocols, few security properties have to be ensured. However, designing a secure cryptographic protocol that ensures several properties at once is a very challenging task. In this paper, we propose a new secure protocol for multipart military missions that involve two independent and distributed decision levels having different security levels. We show that it ensures the secrecy, authentication, and non-repudiation properties. In addition, we show that it resists against man-in-the-middle attacks.

Cloud storage is vulnerable to advanced persistent threats (APTs), in which an attacker launches stealthy, continuous, well-funded and targeted attacks on storage devices. In this paper, cumulative prospect theory (CPT) is applied to study the interactions between a defender of cloud storage and an APT attacker when each of them makes subjective decisions to choose the scan interval and attack interval, respectively. Both the probability weighting effect and the framing effect are applied to model the deviation of subjective decisions of end-users from the objective decisions governed by expected utility theory, under uncertain attack durations. Cumulative decision weights are used to describe the probability weighting effect and the value distortion functions are used to represent the framing effect of subjective APT attackers and defenders in the CPT-based APT defense game, rather than discrete decision weights, as in earlier prospect theoretic study of APT defense. The Nash equilibria of the CPT-based APT defense game are derived, showing that a subjective attacker becomes risk-seeking if the frame of reference for evaluating the utility is large, and becomes risk-averse if the frame of reference for evaluating the utility is small.

Cybersecurity is one of critical issues in modern military operations. In cyber operations, security professionals depend on various information and security systems to mitigate cyber threats through enhanced cyber situational awareness. Cyber situational awareness can give decision makers mission completeness and providing appropriate timely decision support for proactive response. The crucial information for cyber situational awareness can be collected at network boundaries through deep packet inspection with security systems. Regular expression is regarded as a practical method for deep packet inspection that is considering a next generation intrusion detection and prevention, however, it is not commonly used by the reason of its resource intensive characteristics. In this paper, we describe our effort and achievement on regular expression processing capability in real time and an evaluation method with experimental result.

The gradual deployment of intelligent and coordinated devices in the electrical power system needs careful investigation of the interactions between the various domains involved. Especially due to the coupling between ICT and power systems a holistic approach for testing and validating is required. Taking existing (quasi-) standardised smart grid system and test specification methods as a starting point, we are developing a holistic testing and validation approach that allows a very flexible way of assessing the system level aspects by various types of experiments (including virtual, real, and mixed lab settings). This paper describes the formal holistic test case specification method and applies it to a particular co-simulation experimental setup. The various building blocks of such a simulation (i.e., FMI, mosaik, domain-specific simulation federates) are covered in more detail. The presented method addresses most modeling and specification challenges in cyber-physical energy systems and is extensible for future additions such as uncertainty quantification.